NDLI: Improved recursive least squares algorithm based on echo state neural network for nonlinear system identification

Content Provider	IEEE Xplore Digital Library
Author	Song Qingsong Zhao Xiangmo Feng Zuren
Copyright Year	2011
Description	Author affiliation: Systems Engineering Institute, Xi'an Jiaotong University, 710049, China (Feng Zuren) \|\| School of Information Engineering, Chang'an University, 710064, China (Song Qingsong; Zhao Xiangmo)
Abstract	In order to model nonlinear systems with more accuracy, and to further exploit the potential capacities of recurrent neural networks, we propose a novel recursive least square (RLS) algorithm based on echo state network (ESN), and note it as RLSESN in this paper. ESN is a new paradigm for using recurrent neural networks (RNN) with a simpler training method. The proposed RLSESN consists of three main components: an ESN, a recursive least square (RLS) algorithm with adaptive forgetting factor and a change detection module. At first, the change detection module modifies the forgetting factor online according to ESN output errors. And then, the RLS algorithm regulates the ESN output connection weights. The simulation experiment results show that RLSESN can model nonlinear systems very well; the modeling performances are significantly better than those traditional ARMA model based filters.
Starting Page	1692
Ending Page	1695
File Size	153327
Page Count	4
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Training Adaptation models Recurrent neural networks Recursive Least Squares Algorithm Filtering System Identification Neurons Neural Networks Reservoirs Modeling
Content Type	Text
Resource Type	Article

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